RANSAC is a repeating hypothesize-and-verify procedure for parameter estimation and filtering of noise or outlier data. In the traditional approach, this algorithm is evaluated without any prior information on the set of data points which leads to an increase in the number of iterations and compute time. In this paper, we present a GPU based RANSAC algorithm with pre-processing of the assumed sample set of hypothetical inliers by Monte Carlo method. Based on our implementation and results using the Point Cloud Library and NVIDIA CUDA framework for data intensive tasks we obtain significant improvement in the performance of plane segmentation algorithm over the randomly sampled subset of hypothetical inliers. The final consensus set is formed with less number of iterations using our pre-processing model. We can conclude that a pre-processed sample set of hypothetical inliers results in a faster determination of the consensus set consisting of maximum inliers © 2013 IEEE.